3. Application on an Energy Issue: An Empirical Study of China
3.5 Summary
Conventional energy indices, such as energy efficiency and energy intensity, can be used to evaluate how energy inputs are efficiently utilized. However, these indicators neglect the substitution among energy consumption and other factors so that the results obtained from conventional energy indicators overestimate or underestimate the actual state. This paper proposes the total-factor energy productivity index (TFEPI) to assess energy productivity growth for regions in mainland China. TFEPI constructs a multiple-input framework that avoids single-input bias since energy is not the only input to produce economic output. The DEA approach based on the Luenberger index and relative TFEE is applied to conduct a total-factor energy productivity index in this study. The TFEPI proposed in this paper is a dynamic indictor to measure the total-factor energy productivity growth by getting rid of the
Table 3.5
Estimation results of effects of regional characteristics on TFEPI
Dependent Variable: TFEPI
(2) *, **, and *** represent significance at the 10%, 5%, and 1% level, respectively.
(3) Model 1: F test (p-value=0.16); LM test (p-value=0.36); Hausman test (p-value=0.99) (4) Model 2: F test (p-value<0.01); LM test (p-value<0.01); Hausman test (p-value=0.89)
substitution and complement effects among all inputs. It helps provide more insights about efficiency changes as well as technology changes in energy use.
This paper reports the results of an empirical study of regional productivity growth in mainland China. Accordingly, China‟s average total-factor energy productivity was decreasing by 1.4% per year during 2000-2004, especially in the period 2001-2002 (-3.2%).
However, the traditional energy productivity index reveals that China‟s energy productivity change was only decreasing 0.5% annually during the research period. This comparative result shows that the traditional energy productivity index overestimates the energy productivity change if energy is taken as the single input. At the regional level, seven of twenty-night regions enhance their total-factor energy productivity. The TFEPI not only evaluates total-factor energy productivity change, but also appraises change in relative energy
efficiency (catching up effect) and shift in the technology of energy use (innovation effect) by decomposing TFEPI. The finding from a change in relative energy efficiency shows that the whole country‟s average total-factor energy efficiency improves about 0.6% per year and two periods (2001-2003) present negative growth. This indicates that the relative energy efficiency gap between all regions has gradually condensed since 2000. Nevertheless, the results of total-factor energy technical change illustrate that the technology of use energy declines progressively at 2.0% per year during 2000-2004. Over the five years, none of all regions in mainland China shows a positive total-factor energy technical change. We conclude that energy productivity decline in mainland China is attributable to negative technical growth and not relative efficiency change.
What causes regional total-factor productivity inequality and decline in mainland China are important issues in future work. In the present study we only examine the effect of a region‟s development status, economic structure, and energy mix on total-factor energy productivity change, but these effects cannot completely explain the situation of energy productivity in mainland China. Some research studies based on cross-country or cross-region studies suggest that relative energy price may be the key determinants of energy productivity growth (Miketa and Mulder, 2005; Fisher-Vanden et al., 2004). For example, the oil price shows a discrepancy between regions in mainland China, because local governments still have some authority to set the selling price. Hence, the difference in pricing among regions could result in some regions with higher prices (such as Shanghai) having an incentive to improve energy productivity, recommending that additional research focus on the components of the total-factor energy productivity index to draw more precise conclusions about specific effects on energy productivity growth among regions in mainland China. Moreover, it may be of interest for future studies to discuss the contribution of each input variables toward total factor productivity growth. Hence, additional research would
usefully extend the present TFEPI to investigate how the productivity of other input variables change.
4. APPLICATION OF A BANKING ISSUE: AN EMPIRICAL STUDY OF BANKS IN MAINLAND CHINA
4.1 Application background
In the past three decades, China‟s banking system has reformed gradually and gained remarkable successes in many respects. The total assets of the banking industry are at over RMB 60 trillion, or 300 times that in 1978. In November 2009 the capital adequacy ratio and the provision coverage of the banking industry were over 10% and 150%, respectively.
With respect to bank soundness, Industrial and Commercial Bank of China (ICBC), China Construction Bank (CCB), and Bank of China (BOC) are the three largest listed banks in the world. Moreover, the financial reforms have made efficiency and productivity improvements in the banking sector (Chen et al., 2005; Matthews et al., 2009).
This paper aims to investigate the total-factor productivity (TFP) changes and to disaggregate the sources of productivity change in China‟s banking industry from 2005 to 2009. China‟s „Big Four‟ state-owned banks (SOBs) have been partially privatized to take on minority foreign ownership since 2005. However, the academic literature related to bank productivity mainly focuses on U.S. and European banks, using the Malmquist productivity index and Luenberger productivity index approaches.
One of the first studies to investigate productivity change in the banking industry is Berg et al. (1992), who employee the Malmquist index for productivity growth and find the source of productivity growth is efficiency improvement in Norway‟s banks during the years 1980-89. Other evidence indicates that productivity growth is mainly driven by technical change in the U.S. (Alam, 2001; Mukherjee et al., 2001) and European banks (e.g., Casu et al., 2004; Koutsomanoli-Filippaki et al., 2009; Barros et al., 2010) by applying the Malmquist index or Luenberger index.
Few research studies have taken a look at the productivity growth of Chinese banks.
Kumbhakar and Wang (2007) use the input distance function to analyze the efficiency and TFP change of 14 Chinese banks during 1993-2002. They suggest that joint-stock banks (JSBs) are more efficient and gain a higher TFP growth rate than SOBs. Matthews et al.
(2009) apply the Malmquist index with a bootstrap method to evaluate the productivity change for 14 Chinese banks from 1997 to 2006. They indicate that JSBs generally show a better performance than SOBs, while there is no productivity growth for the SOBs since technological progress is offset by efficiency regression.
In summary, it is found that prior literature adopts the Malmquist productivity index or Luenberger productivity index to investigate the change of TFP, efficiency change, and technical change. Unfortunately, these two indices are aggregative indices and cannot deal with the productivity change of a single factor under a total-factor framework, meaning insights may be lacking if we want to investigate the productivity change of one particular factor among all input factors (such as labor, capital, and fund inputs). This paper tries to overcome the disadvantage of the total-factor productivity index and introduces an index to measure the productivity change of an individual factor under a total-factor framework.
The proposed index, the so-called total-factor input productivity index (TIPI), uses a Färe-Lovell efficiency measure to extend the traditional Luenberger productivity index and finds out the strongly efficient vector for each input. This index then can be decomposed into total-factor input efficiency change and total-factor input technical change, meaning that we can discuss the sources of individual input productivity. Furthermore, we will show that the TFP change is the average of the productivity change of individual input.
The remainder of this chapter is organized as follows. Section 4.2 describes an extended model. Section 4.3 interprets the data sources and variables‟ descriptions.
Section 4.4 provides the empirical results.
4.2 Extended model
This application extends the model introduced in Chapter 2. I illustrate the approach of extended TIPI as follows: Assume there are M inputs and S outputs for each N objects in each time period of T. The ith input and rth output variable of the jth object are represented by xijt and ytrj in time t, respectively. Briec (2000) introduces a Färe-Lovell efficiency measure that has the advantage to select a strong efficient vector onto the frontier. Therefore, the input-oriented directional distance functions for the observation o in time t can be stated as the following linear programming problems:
where λj is the intensity variable that serves to form a convex combination of observed inputs and outputs. It is noteworthy that the Färe-Lovell efficiency measure is based on the constant return to scale assumption, indicating the efficient level of inputs and outputs for achieving overall technical efficiency.
The other three distance functions in equation (2.3) can be calculated straightforward according to equation (4.1). The computation of D(t1)(xt1,yt1) is exactly like equation (4.1), where t+1 is substituted for t. A similar approach is adopted for two intertemporal directional distance functions, i.e., D( )t (xt1,yt1) and D(t1)( ,x yt t). It is noted that these two intertemporal directional distance functions need not be greater than or equal to zero.
Therefore, the Luenberger productivity index for total factors can be computed based on
equations (2.3) and (4.1).
With respect to TIPI, we further define βi obtained from equation (4.1) as Di t( )( ,x yt t), meaning that Di t( )( ,x yt t) is the distance function for the ith input variable at t under a total-factor framework. Accordingly, the TIPI for the ith input can be measured as follows:
Note that if the value of TIPI is less than, equal to, or greater than zero, then it indicates the productivity of the ith input regresses, does not change, or progresses from period t to t+1.
TIPI is only an aggregate index that might be oversimplified or over-aggregated. In other words, although TIPI computes the total-factor input productivity change, it does not indicate the sources of change directly. Thus, a more deep study on the components of TIPI is necessary. Based on the traditional Luenberger productivity index, TIPI can be further decomposed into two components: efficiency change (EFFCH) and technical change (TECHCH). The former component measures the change in relative efficiency and the latter measures the shift in the technology of the ith input used:
1 1 inputs, we decompose the TFP change into the productivity change of individual input as:
individual input productivity, and the efficiency change and technical change of individual input can be aggregated as the total-factor efficiency change and technical change, respectively.
4.3 Data and variables‟ descriptions
The literature typically applies two approaches to evaluate bank efficiency and productivity. One is the intermediation approach, which is based on the main function of the bank as a financial intermediary. Another is the production approach, which views banks as the producers of financial services. Under the intermediation approach, this article specifies two outputs and three inputs to investigate the total-factor input productivity change of banks.
The output variables encompass total loans (TL) and other earning assets (OEA).3 These output variables are commonly adopted in previous literature, such as Berger et al. (2009) and Bonin et al. (2005). It is noteworthy that the quality of loans (e.g., non-performing loans or problem loans) has received more emphasis in recent studies. Therefore, loan loss reserves are subtracted from total loans in order to ensure that this output is of comparable quality.
With respect to input variables, labor (employees), physical capital, and funds are the conventional inputs in previous research (Altunbas et al., 2001; Beccalli et al., 2006). Funds (F) define total deposits and short-term funding; capital (C) measures total fixed assets; labor (L) is the total number of bank employees.
This application collects a balanced panel data covering 2005-2009 from 21 Chinese commercial banks, including the Big Four state-owned banks, national shareholding commercial banks, and major city commercial banks in mainland China. All financial data,
3 Other earning assets include (1) loans and advances to banks, (2) trading securities and at FV through income, (3) derivatives, (4) available for sale securities, (5) held to maturity securities, (6) at-equity investments in
such as the items of balance sheets and income statements, are taken from Bankscope database, a comprehensive resource of international banking institutions. Unfortunately, the information on the numbers of employees for each Chinese bank is quite incomplete in the database. Therefore, this variable is complemented through each bank‟s annual report.
All nominal prices are transferred using the GDP deflator with 2009 as the base year.
Table 4.1 summarizes the output and input data of our sample from 2005 to 2009. It is noteworthy that the high standard deviations of all variables indicate that the Big Four state-owned banks dominate China‟s bank industry. The correlations between each pair of input-output variables are highly positive, which is consistent with economic intuition and production theory. OEA - million RMB Total other earning assets 899,962 1,384,179 F (Funds)
L (Labor) - person Numbers of employees 76,176 135,012
Sources: Bankscope database and each bank‟s annual report.
4.4 Empirical results
This section first illustrates the total-factor productivity growth, individual input productivity change, and the decomposition of productivity change at the industry level. It then presents and discusses the empirical results at the firm level.
associates, (7) other securities, (8) investment in property, (9) insurance assets, and (10) other earning assets.
4.3.1 Productivity analysis at the industry level
Figure 4.1 shows the annual total-factor productivity growth and productivity changes of three inputs from 2005 to 2009. The average annual TFP growth rate is 3.79% and the TFP cumulatively grows by 15.81%, indicating an upward trend for Chinese banks. All sub-periods present a positive TFP growth rate except the period of 2007-2008 (-1.24%).
One reasonable explanation is that the global financial crisis impacted quite negatively the international banking sector, and even Chinese banks could not escape from it in 2008.
Figure 4.1 Annual change of TFP and total-factor input productivity
Figure 4.1 indicates the productivity change of three inputs under a total-factor framework. During 2005-2007, three inputs have positive productivity change, especially for capital used. Capital and fund productivity improve 2.56% and 0.02% from 2007 to 2008, respectively. Although labor productivity of Chinese banks decreases 6.30% from 2007 to 2008, it outstandingly improves 18.6% in the last sub-period. In summary, capital, labor, and fund productivity cumulatively change 36.00%, 12.32%, and -0.22% over the
research period, respectively. We conclude that TFP‟s improvement is mainly contributed by capital management and human resource reinforcement in China‟s bank industry.
With respect to the source of TFP growth, the literature mostly decomposes TFP into technical change and efficiency change. Hence, this paper illustrates these two components in Figure 4.2. Figure 4.2 sketches the cumulative growth of TFP, technical change, and efficiency change during 2005-2009. Accordingly, there is a strictly increasing trend of technical change, meaning that the production frontier substantially shifts upward. However, there is no catch-up effect in the bank industry since the change in relative efficiency totally decreases 3.84% from 2005 to 2009. This indicates that inefficient banks are getting farther from the annual frontier in China‟s banking sector.
Figure 4.2 Cumulative changes of TFP and its components
As mentioned above, the TFP drops during 2007-2008. Figure 4.2 shows that a plunge in efficiency change results in a decline for TFP in this period. In other words, technical progress is swamped by average efficiency losses from 2007 to 2008. Hence, we summarize that the TFP gains are principally driven by technical progress. In general, this result is consistent with previous findings in Kumbhakar and Wang (2007) and Matthews et al. (2009).
Aside from the two components (i.e., technical progress and efficiency change) of TFP, we further decompose the productivity growth of individual inputs into those two components.
The upside of Table 4.2 provides the annually technical change of three inputs under a total-factor framework in each sub-period. The technology of capital used gains the highest growth rate with 11.40% annually, while that of labor resource improves 3.18% on average.
However, the technology of funds used slightly regresses with a rate of 0.33% annually.
Hence, we consider that the technical progress of capital usage is the main source of the total-factor technology shift.
Table 4.2
Annually technical changes and efficiency changes of three inputs
Period Technical Change
The lower panel of Table 4.2 lists the annual efficiency change of three inputs under a total-factor framework in each sub-period. This result shows that fund and labor inputs both present positive efficiency changes on average, although these changes are relatively small.
It also implies that the gaps in the relative efficiency of those two inputs gradually narrow among Chinese banks. Nevertheless, the average efficiency of capital input decreases year
in China‟s bank industry. Only for the last period does one see that efficiency improvement of TFP results from all inputs‟ efficiency enhancement.
4.3.2 Productivity analysis at the firm level
This subsection compares the productivity growth, including TFP and three inputs, among 21 Chinese banks. Table 4.3 lists the productivity change of TFP and three inputs as well as the decomposition of each productivity indicator. From the viewpoint of TFP change, five banks are „innovators‟, meaning that these banks construct the efficiency frontier each year and cause the frontier to shift. However, if we further investigate individual input productivity, only Bank of Beijing (#2) is an innovator that shifts the frontiers of all inputs, especially for the technical progress of capital use.
According to Table 4.3, one bank (Bank of China) shows a negative growth of TFP (-0.84%) among 21 Chinese banks. The decline of TFP results from its efficiency regress (9.68%), though its technical change is positive (8.84%) during 2005-2009. Furthermore, the total-factor input productivity changes of Bank of China decrease about 0.12% to 1.72%
for three inputs. This result shows that the drops of inputs‟ productivity can be attributed to the efficiency changes of capital (-15.12%) and labor (-13.49%) usage.
Aside the six banks discussed above, other banks tend to fall into one of three categories based on Table 4.3. The first group indicates that the total-factor productivity growth is mainly driven by efficiency improvement. These banks include China CITIC Bank (#8) and Evergrowing Bank (#14). Nevertheless, it is worth noting that the major sources of efficiency improvement of China CITIC Bank and Evergrowing Bank are capital efficiency (11.60%) and labor efficiency (14.62%), respectively.
The second group includes Agricultural Bank of China (#1), Bank of Communications (#4), Guangdong Development Bank (#15), and ICBC (#17). All of these banks have the
Table 4.3
Productivity growth, technical progress, and efficiency change of TFP and individual input
Note: Banks #1-#21 are Agricultural Bank of China (1), Bank of Beijing Co. Ltd. (2), Bank of China Limited (3), Bank of Communications Co. Ltd. (4), Bank of Nanjing (5),
Bank of Ningbo (6), Bank of Shanghai (7), China CITIC Bank Corporation Limited (8), China Construction Bank Corporation (9), China Everbright Bank Co. Ltd. (10), China Merchants Bank Co. Ltd. (11), China Minsheng Banking Corporation (12), China Zheshang Bank Co. Ltd. (13), Evergrowing Bank Co. Ltd. (14), Guangdong Development Bank (15), Hua Xia Bank (16), Industrial & Commercial Bank of China (17), Industrial Bank Co. Ltd. (18), Shanghai Pudong Development Bank (19), Shanghai Rural Commercial Bank (20), and Shenzhen Development Bank Co. Ltd. (21), respectively.
Bank Total-Factor Fund Capital Labor
TFPCH TECHCH EFFCH FPCH TECHCH EFFCH CPCH TECHCH EFFCH LPCH TECHCH EFFCH
characteristics that the TFP growth is mainly driven by the technical progress. However, there are some differences between those banks when we further analyze total-factor input productivity of individual input. For examples, ICBC is the only one in which all input productivity changes are positive and the components (technical change and efficiency change) of three input productivities are also positive. Agricultural Bank of China presents a slight technical improvement (0.40% per year) of TFP, while this progress is caused by the technological progress of capital usage, and not the other two inputs.
The last group containing nine banks presents that the technological gains transcend the efficiency regressions and results in TFP growth. From the view of individual input, the sources of TFP growth are capital productivity improvement and the technical progress of capital use for those banks. It confirms that the TIPI proposed by this paper is necessary in order to investigate the source of TFP in more detail.
4.3.3 Further analysis of productivity and efficiency
The previous subsection illustrates the results of the total-factor inputs‟ productivity growth and their decompositions. However, it is noteworthy to simultaneously consider banks‟ static efficiency level and dynamic productivity change. Therefore, the following analysis focuses on banks‟ relative efficiency and productivity change in order to obtain more insights about each bank‟s advantages and disadvantages.
First of all, this paper uses the industry‟s mean efficiency score and productivity change rate to construct an efficiency-productivity matrix for each input variable.4 Banks at the first
First of all, this paper uses the industry‟s mean efficiency score and productivity change rate to construct an efficiency-productivity matrix for each input variable.4 Banks at the first